What to Expect From Artificial Intelligence (Continued from page 23)
apples in finer detail, but in
the real world, the amount
of complexity increases
exponentially.

Environments with a highdegree of complexity are wheremachine learning is most useful.

In one type of training, the machine is shown a set of pictures
with names attached. It is then
shown millions of pictures that
each contain named objects,
only some of which are apples.

As a result, the machine notices
correlations — for example,
apples are often red. Using correlates such as color, shape,
texture, and, most important,
context, the machine references
information from past images
of apples to predict whether an
unidentified new image it’s
viewing contains an apple.

When we talk about prediction, we usually mean
anticipating what will happen
in the future. For example,
machine learning can be used
to predict whether a bank customer will default on a loan.

But we can also apply it to the
present by, for instance, using
symptoms to develop a medical diagnosis (in effect,
predicting the presence of a
disease). Using data this way is
not new. The mathematical
ideas behind machine learning
are decades old. Many of the
algorithms are even older.

So what has changed?

Recent advances in computa-tional speed, data storage, dataretrieval, sensors, and algorithmshave combined to dramaticallyreduce the cost of machinelearning-based predictions. Andthe results can be seen in thespeed of image recognition andlanguage translation, which havegone from clunky to nearly per-fect. All this progress has resultedin a dramatic decrease in the costof prediction.

The Value ofPrediction

So how will improvements in
machine learning impact what
happens in the workplace?

How will they affect one’s ability to complete a task, which
might be anything from driving a car to establishing the
price for a new product? Once
actions are taken, they generate
outcomes. (See “The Anatomy
of a Task.”) But actions don’t
occur in a vacuum. Rather,
they are shaped by underlying
conditions. For example, a
driver’s decision to turn right
or left is influenced by predictions about what other drivers
will do and what the best
course of action may be in light
of those predictions.

Seen in this way, it’s useful to
distinguish between the cost
versus the value of prediction.

As we have noted, advances inAI have reduced the cost of pre-diction. Just as important iswhat has happened to thevalue. Prediction becomesmore valuable when data ismore widely available andmore accessible. The computerrevolution has enabled hugeincreases in both the amountand variety of data. As dataavailability expands, predictionbecomes increasingly possiblein a wider variety of tasks.

Autonomous driving offers
a good example. The technology required for a car to
accelerate, turn, and brake
without a driver is decades old.

Engineers initially focused ondirecting the car with whatcomputer scientists call “ifthen else” algorithms, such as“If an object is in front of thecar, then brake.” But progresswas slow; there were too manypossibilities to codify every-thing. Then, in the early 2000s,several research groups pur-sued a useful insight: A vehiclecould drive autonomously bypredicting what a humandriver would do in response toa set of inputs (inputs that, inthe vehicle’s case, could comefrom camera images, informa-tion using the laser-basedmeasurement method knownas LIDAR, and mapping data).The recognition that autono-mous driving was a predictionproblem solvable with ma-chine learning meant thatautonomous vehicles couldstart to become a reality in themarketplace years earlier thanhad been anticipated.

Who Judges?

Judgment is the ability tomake considered decisions —to understand the impactdifferent actions will have onoutcomes in light of predic-tions. Tasks where the desiredoutcome can be easily de-scribed and there is limitedneed for human judgment aregenerally easier to automate.For other tasks, describing aprecise outcome can be moredifficult, particularly when thedesired outcome resides in theminds of humans and cannotbe translated into something amachine can understand.This is not to say that ourunderstanding of humanjudgment won’t improve andtherefore become subject toautomation. New modes ofmachine learning may findways to examine the relation-ships between actions andoutcomes, and then use theinformation to improve pre-dictions. We saw an exampleof this in 2016, when AlphaGo,Google’s DeepMind artificialintelligence program, suc-ceeded in beating one of thetop players in the world in thegame of Go. AlphaGo honedits capability by analyzingthousands of human-to-humanGo games and playing againstTHE ANATOMY OF A TASK

Actions are shaped by the underlying conditions and the resolution of uncertainty to lead to outcomes. Drivers, for example,
need to observe the immediate environment and make adjustments to minimize the risk of accidents and avoid bottlenecks.
In doing so, they use judgment in combination with prediction.